An intellectual optimization of k-anonymity model for efficient privacy preservation in cloud platform

Abstract

Concerns of security as well as privacy are the chief obstacles which have prevented the public cloud’s extensive adoption in Intel IT as well as across the industry. Generally, IT organizations are quite reluctant to store sensitive as well as valuable data in infrastructures which are out of their control. The technique of anonymization is employed by enterprises to raise the security of the public cloud’s data whilst facilitating the data’s analysis as well as application. The procedure of data anonymization will modify how the data is either employed or published in such a way that it will prevent the key information’s identification. The privacy issues are addressed using k-anonymity. However, the issue of selecting the variables for anonymization and suppression of variables without the loss of knowledge is an optimization problem. To address the selection of variables for anonymization and suppression, metaheuristic algorithms are used. Diverse research groups have successfully utilized the River Formation Dynamics (RFD) metaheuristic to handle numerous problems of discrete combinatorial optimization. Even so, this metaheuristic has never been adapted for use in domains of continuous optimization. To mitigate the local minima problem, hybridization of the algorithms is proposed. In this work, a modified K-Anonymity technique’s proposal has been given by using the Modified Hill Climbing (MHC) optimization, the RFD-MHC optimization, the RFD-PSO optimization, the RFD-MHC suppression as well as the RFD-PSO suppression. Furthermore, proposal for a suppression technique has also been given in this work. Experiments demonstrated that the RFD-PSO optimization has higher classification accuracy in the range of 6.73% to 8.55% when compared to manual K-anonymization. The work has also given better trade off for security analysis and data utility effectiveness.